code: def __call__(self, features: List[Dict[str, Any]]) -> Dict[str, Any]:
      features_j = []
      features_k = []
      for feature in features:
          features_j.append(
              {
                  "input_ids": feature["input_ids_j"],
                  "attention_mask": feature["attention_mask_j"],
              }
          )
          features_k.append(
              {
                  "input_ids": feature["input_ids_k"],
                  "attention_mask": feature["attention_mask_k"],
              }
          )
      batch_j = self.tokenizer.pad(
          features_j,
          padding=self.padding,
          max_length=self.max_length,
          pad_to_multiple_of=self.pad_to_multiple_of,
          return_tensors=self.return_tensors,
      )
      batch_k = self.tokenizer.pad(
          features_k,
          padding=self.padding,
          max_length=self.max_length,
          pad_to_multiple_of=self.pad_to_multiple_of,
          return_tensors=self.return_tensors,
      )
      batch = {
          "input_ids_j": batch_j["input_ids"],
          "attention_mask_j": batch_j["attention_mask"],
          "input_ids_k": batch_k["input_ids"],
          "attention_mask_k": batch_k["attention_mask"],
          "return_loss": True,
      }
      return batch
  
file_path: trl/examples/research_projects/stack_llama/scripts/reward_modeling.py
imports: from dataclasses import dataclass, field
  from typing import Any, Dict, List, Optional, Union
  import evaluate
  import numpy
  import torch
  import torch.nn
  from datasets import load_dataset
  from peft import LoraConfig, TaskType, get_peft_model
  from transformers import AutoModelForSequenceClassification, AutoTokenizer, HfArgumentParser, PreTrainedTokenizerBase, Trainer, TrainerCallback, TrainingArguments
  from transformers.utils import PaddingStrategy
label_desc: Use image node_id 1 for calling the RewardDataCollatorWithPadding obj's underlying member method code with example usage: obj.__call__(features) and returns: batch